Towards a hierarchical parametrization to address prior uncertainty in ensemble-based data assimilation

DOI10.1007/s10596-015-9545-0zbMath1392.86044OpenAlexW2157573406MaRDI QIDQ722777

Alexandre Anozé Emerick

Publication date: 27 July 2018

Published in: Computational Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10596-015-9545-0

zbMATH Keywords

Gaussian mixturedata assimilationhistory matching techniquesprior uncertainty

Mathematics Subject Classification ID

Inverse problems in geophysics (86A22) Geostatistics (86A32) Computational methods for problems pertaining to geophysics (86-08)

Related Items (1)

Parameterizations for ensemble Kalman inversion

Uses Software

• EnKF
• SGeMS

Cites Work

• Unnamed Item
• Multimodal ensemble Kalman filtering using Gaussian mixture models
• Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter vari\-ants
• Improving the ensemble estimate of the Kalman gain by bootstrap sampling
• Levenberg-Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification
• Investigation of the sampling performance of ensemble-based methods with a simple reservoir model
• Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter
• The Bayesian Choice
• Data Assimilation

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